Wed, 23 Jan 2019

New package ZVCV with initial version 1.0.0
Package: ZVCV
Type: Package
Title: Zero-Variance Control Variates
Version: 1.0.0
Date: 2018-12-20
Authors@R: c(person("Leah F.","South",role=c("aut","cre"),email="leah.south@hdr.qut.edu.au",comment = c(ORCID = "0000-0002-5646-2963")))
Description: Zero-variance control variates (ZV-CV, Mira et al. (2013) <doi:10.1007/s11222-012-9344-6>) is a post-processing method to reduce the variance of Monte Carlo estimators of expectations using the derivatives of the log target. Once the derivatives are available, the only additional computational effort is in solving a linear regression problem. Recently, this method has been extended to higher dimensions using regularisation (South et al., 2018 <arXiv:1811.05073>). This package can be used to easily perform ZV-CV or regularised ZV-CV when a set of samples, derivatives and function evaluations are available. Additional functions for applying ZV-CV to two estimators for the normalising constant of the posterior distribution in Bayesian statistics are also supplied.
License: GPL (>= 2)
LazyLoad: yes
Imports: Rcpp (>= 0.11.0), glmnet, abind, mvtnorm, partitions, stats
LinkingTo: Rcpp, RcppArmadillo
LazyData: true
Encoding: UTF-8
RoxygenNote: 6.1.1
NeedsCompilation: yes
Packaged: 2019-01-16 02:03:43 UTC; n8001626
Author: Leah F. South [aut, cre] (<https://orcid.org/0000-0002-5646-2963>)
Maintainer: Leah F. South <leah.south@hdr.qut.edu.au>
Repository: CRAN
Date/Publication: 2019-01-24 00:00:02 UTC

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Package rym updated to version 0.5.2 with previous version 0.5.1 dated 2019-01-17

Title: R Interface to Yandex Metrica API
Description: Allows work with 'Management API' for load counters, segments, filters, user permissions and goals list from Yandex Metrica, 'Reporting API' allows you to get information about the statistics of site visits and other data without using the web interface, 'Logs API' allows to receive non-aggregated data and 'Compatible with Google Analytics Core Reporting API v3' allows receive information about site traffic and other data using field names from Google Analytics Core API. For more information see official documents <https://tech.yandex.ru/metrika/doc/api2/concept/about-docpage/>.
Author: Alexey Seleznev <selesnow@gmail.com>
Maintainer: Alexey Seleznev <selesnow@gmail.com>

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Package ROI updated to version 0.3-2 with previous version 0.3-1 dated 2018-01-26

Title: R Optimization Infrastructure
Description: The R Optimization Infrastructure ('ROI') is a sophisticated framework for handling optimization problems in R. More information can be found on the 'ROI' homepage <http://roi.r-forge.r-project.org/>.
Author: Kurt Hornik [aut], David Meyer [aut], Florian Schwendinger [aut], Stefan Theussl [aut, cre], Diethelm Wuertz [ctb]
Maintainer: Stefan Theussl <Stefan.Theussl@R-Project.org>

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New package portalr with initial version 0.2.1
Package: portalr
Title: Create Useful Summaries of the Portal Data
Version: 0.2.1
Authors@R: c(person(c("Glenda", "M."), "Yenni", role = c("aut", "cre"), email = "glenda@weecology.org", comment = c(ORCID = "0000-0001-6969-1848")), person("Hao", "Ye", role = c("aut"), comment = c(ORCID = "0000-0002-8630-1458")), person(c("Erica", "M."), "Christensen", role = c("aut"), comment = c(ORCID = "0000-0002-5635-2502")), person(c("Juniper", "L."), "Simonis", role = c("aut"), comment = c(ORCID = "0000-0001-9798-0460")), person(c("Ellen", "K."), "Bledsoe", role = c("aut"), comment = c(ORCID = "0000-0002-3629-7235")), person(c("Renata", "M."), "Diaz", role = c("aut"), comment = c(ORCID = "0000-0003-0803-4734")), person(c("Shawn", "D."), "Taylor", role = c("aut"), comment = c(ORCID = "0000-0002-6178-6903")), person(c("Ethan", "P,"), "White", role = c("aut"), comment = c(ORCID = "0000-0001-6728-7745")), person(c("S.K.", "Morgan"), "Ernest", role = c("aut"), comment = c(ORCID = "0000-0002-6026-8530")))
Description: Download and generate summaries for the rodent, plant, ant, and weather data from the Portal Project. Portal is a long-term (and ongoing) experimental monitoring site in the Chihuahua desert. The raw data files can be found at <https://github.com/weecology/portaldata>.
License: MIT + file LICENSE
URL: https://weecology.github.io/portalr/, https://github.com/weecology/portalr
BugReports: https://github.com/weecology/portalr/issues
LazyData: true
Depends: R (>= 3.2.3)
Imports: dplyr, ggplot2, tidyr, zoo, lubridate, magrittr, httr, rlang, forecast, lunar, jsonlite, tibble
Suggests: httptest, digest, tidyverse, cowplot, knitr, rmarkdown, pkgdown, covr
RoxygenNote: 6.1.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-01-23 19:54:53 UTC; GlendaYenni
Author: Glenda M. Yenni [aut, cre] (<https://orcid.org/0000-0001-6969-1848>), Hao Ye [aut] (<https://orcid.org/0000-0002-8630-1458>), Erica M. Christensen [aut] (<https://orcid.org/0000-0002-5635-2502>), Juniper L. Simonis [aut] (<https://orcid.org/0000-0001-9798-0460>), Ellen K. Bledsoe [aut] (<https://orcid.org/0000-0002-3629-7235>), Renata M. Diaz [aut] (<https://orcid.org/0000-0003-0803-4734>), Shawn D. Taylor [aut] (<https://orcid.org/0000-0002-6178-6903>), Ethan P, White [aut] (<https://orcid.org/0000-0001-6728-7745>), S.K. Morgan Ernest [aut] (<https://orcid.org/0000-0002-6026-8530>)
Maintainer: Glenda M. Yenni <glenda@weecology.org>
Repository: CRAN
Date/Publication: 2019-01-23 22:50:35 UTC

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Package kutils updated to version 1.60 with previous version 1.45 dated 2018-05-19

Title: Project Management Tools
Description: Tools for data importation, recoding, and inspection that are used at the University of Kansas Center for Research Methods and Data Analysis. There are functions to create new project folders, R code templates, create uniquely named output directories, and to quickly obtain a visual summary for each variable in a data frame. The main feature here is the systematic implementation of the "variable key" framework for data importation and recoding. We are eager to have community feedback about the variable key and the vignette about it.
Author: Paul Johnson [aut, cre], Benjamin Kite [aut], Charles Redmon [aut], Jared Harpole [ctb], Kenna Whitley [ctb], Po-Yi Chen [ctb], Shadi Pirhosseinloo [ctb]
Maintainer: Paul Johnson <pauljohn@ku.edu>

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New package blscrapeR with initial version 3.1.5
Package: blscrapeR
Type: Package
Title: An API Wrapper for the Bureau of Labor Statistics (BLS)
Version: 3.1.5
Authors@R: person("Kris", "Eberwein", email = "eberwein@knights.ucf.edu", role = c("aut", "cre"))
Description: Scrapes various data from <https://www.bls.gov/>. The U.S. Bureau of Labor Statistics is the statistical branch of the United States Department of Labor. The package has additional functions to help parse, analyze and visualize the data.
Depends: R (>= 3.3.0)
Imports: httr, jsonlite, ggplot2, magrittr, utils, stats, grDevices, dplyr, purrr, tibble, stringr
Suggests: testthat, knitr, rmarkdown
License: MIT + file LICENSE
URL: https://github.com/keberwein/blscrapeR
BugReports: https://github.com/keberwein/blscrapeR/issues
LazyData: true
RoxygenNote: 6.1.0
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-01-23 20:20:57 UTC; mediacenter
Author: Kris Eberwein [aut, cre]
Maintainer: Kris Eberwein <eberwein@knights.ucf.edu>
Repository: CRAN
Date/Publication: 2019-01-23 22:50:59 UTC

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Package asbio updated to version 1.5-5 with previous version 1.5-4 dated 2019-01-09

Title: A Collection of Statistical Tools for Biologists
Description: Contains functions from: Aho, K. (2014) Foundational and Applied Statistics for Biologists using R. CRC/Taylor and Francis, Boca Raton, FL, ISBN: 978-1-4398-7338-0.
Author: Ken Aho
Maintainer: Ken Aho <kenaho1@gmail.com>

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Package taxotools updated to version 0.0.5 with previous version 0.0.4 dated 2018-09-28

Title: Tools to Handle Taxonomic Lists
Description: Some tools to work with taxonomic name lists.
Author: Vijay Barve [aut, cre] (<https://orcid.org/0000-0002-4852-2567>)
Maintainer: Vijay Barve <vijay.barve@gmail.com>

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Package rtext updated to version 0.1.21 with previous version 0.1.20 dated 2016-11-01

Title: R6 Objects for Text and Data
Description: For natural language processing and analysis of qualitative text coding structures which provide a way to bind together text and text data are fundamental. The package provides such a structure and accompanying methods in form of R6 objects. The 'rtext' class allows for text handling and text coding (character or regex based) including data updates on text transformations as well as aggregation on various levels. Furthermore, the usage of R6 enables inheritance and passing by reference which should enable 'rtext' instances to be used as back-end for R based graphical text editors or text coding GUIs.
Author: Peter Meissner [aut, cre], Ulrich Sieberer [cph], University of Konstanz [cph]
Maintainer: Peter Meissner <retep.meissner@gmail.com>

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Package episheet updated to version 0.4.0 with previous version 0.3.0 dated 2018-10-22

Title: Rothman's Episheet
Description: A collection of R functions supporting the text book Modern Epidemiology, Second Edition, by Kenneth J.Rothman and Sander Greenland. ISBN 13: 978-0781755641 See <http://www.krothman.org/> for more information.
Author: James Black [aut, cre], Ken Rothman [cph], Simon Thelwall [ctb] (<https://orcid.org/0000-0002-0434-2724>)
Maintainer: James Black <james@epijim.uk>

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Package freqweights (with last version 1.0.4) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2017-05-12 1.0.4
2017-04-18 1.0.3
2015-06-22 1.0.2
2014-10-09 1.0.1
2014-09-01 0.1.1
2014-05-21 0.1.0
2014-04-13 0.0.1

Permanent link
Package simglm updated to version 0.7.2 with previous version 0.7.1 dated 2018-07-25

Title: Simulate Models Based on the Generalized Linear Model
Description: Easily simulates regression models, including both simple regression and generalized linear mixed models with up to three level of nesting. Power simulations that are flexible allowing the specification of missing data, unbalanced designs, and different random error distributions are built into the package.
Author: Brandon LeBeau [aut, cre]
Maintainer: Brandon LeBeau <lebebr01+simglm@gmail.com>

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Package prc updated to version 2019.1-23 with previous version 2018.8-17 dated 2018-08-17

Title: Paired Response Curve
Description: Estimation, prediction and testing for analyzing serial dilution assay data (Fong and Yu 2015 <DOI:10.1080/19466315.2015.1093019>) using paired response curve, which is a model of the nonlinear functional relationship between experimental outcomes measured at two different dilutions.
Author: Youyi Fong
Maintainer: Youyi Fong <youyifong@gmail.com>

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Package ggmcmc updated to version 1.1.1 with previous version 1.1 dated 2016-06-28

Title: Tools for Analyzing MCMC Simulations from Bayesian Inference
Description: Tools for assessing and diagnosing convergence of Markov Chain Monte Carlo simulations, as well as for graphically display results from full MCMC analysis. The package also facilitates the graphical interpretation of models by providing flexible functions to plot the results against observed variables.
Author: Xavier Fernández i Marín <xavier.fim@gmail.com>
Maintainer: ORPHANED

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Package mrgsolve updated to version 0.9.0 with previous version 0.8.12 dated 2018-04-27

Title: Simulate from ODE-Based Models
Description: Fast simulation from ordinary differential equation (ODE) based models typically employed in quantitative pharmacology and systems biology.
Author: Kyle T Baron [aut, cre] (<https://orcid.org/0000-0001-7252-5656>), Alan C Hindmarsh [ctb], Linda R Petzold [ctb], Bill Gillespie [ctb], Charles Margossian [ctb], Devin Pastoor [ctb], Bill Denney [ctb] (<https://orcid.org/0000-0002-5759-428X>), Metrum Research Group [cph]
Maintainer: Kyle T Baron <kyleb@metrumrg.com>

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 mrgsolve-0.9.0/mrgsolve/tests/testthat/test-mrgsim_q.R        |only
 mrgsolve-0.9.0/mrgsolve/tests/testthat/test-nmxml.R           |   32 
 mrgsolve-0.9.0/mrgsolve/tests/testthat/test-obsonly-obsaug.R  |   10 
 mrgsolve-0.9.0/mrgsolve/tests/testthat/test-opts.R            |   22 
 mrgsolve-0.9.0/mrgsolve/tests/testthat/test-param.R           |only
 mrgsolve-0.9.0/mrgsolve/tests/testthat/test-rcpp_globals.R    |    2 
 mrgsolve-0.9.0/mrgsolve/tests/testthat/test-realize.R         |    2 
 mrgsolve-0.9.0/mrgsolve/tests/testthat/test-records.R         |    4 
 mrgsolve-0.9.0/mrgsolve/tests/testthat/test-relabel.R         |    2 
 mrgsolve-0.9.0/mrgsolve/tests/testthat/test-rename.R          |   16 
 mrgsolve-0.9.0/mrgsolve/tests/testthat/test-rng.R             |    2 
 mrgsolve-0.9.0/mrgsolve/tests/testthat/test-tad.R             |    2 
 mrgsolve-0.9.0/mrgsolve/tests/testthat/test-update.R          |   82 -
 mrgsolve-0.9.0/mrgsolve/tests/testthat/test-utils.R           |   19 
 237 files changed, 3387 insertions(+), 2617 deletions(-)

More information about mrgsolve at CRAN
Permanent link

Package landscapemetrics updated to version 0.3.1 with previous version 0.3 dated 2018-12-07

Title: Landscape Metrics for Categorical Map Patterns
Description: Calculates landscape metrics for categorical landscape patterns in a tidy workflow. 'landscapemetrics' reimplements the most common metrics from 'FRAGSTATS' (<https://www.umass.edu/landeco/research/fragstats/fragstats.html>) and new ones from the current literature on landscape metrics. This package supports 'raster' spatial objects and takes RasterLayer, RasterStacks, RasterBricks or lists of RasterLayer from the 'raster' package as input arguments. It further provides utility functions to visualize patches, select metrics and building blocks to develop new metrics.
Author: Maximillian H.K. Hesselbarth [aut, cre] (<https://orcid.org/0000-0003-1125-9918>), Marco Sciaini [aut] (<https://orcid.org/0000-0002-3042-5435>), Jakub Nowosad [aut] (<https://orcid.org/0000-0002-1057-3721>), Sebastian Hanss [aut] (<https://orcid.org/0000-0002-3990-4897>), Laura J. Graham [ctb] (Input on package structure), Jeffrey Hollister [ctb] (Input on package structure), Kimberly A. With [ctb] (Input on package structure), Florian Privé [ctb] (Original author of underlying C++ code for get_nearestneighbour() function), Jeremy VanDerWal [ctb] (Original author of underlying C code for get_patches() function), Matt Strimas-Mackey [ctb] (Bugfix in sample_metrics())
Maintainer: Maximillian H.K. Hesselbarth <maximilian.hesselbarth@uni-goettingen.de>

Diff between landscapemetrics versions 0.3 dated 2018-12-07 and 0.3.1 dated 2019-01-23

 DESCRIPTION                                       |    6 
 MD5                                               |  646 +--
 NEWS.md                                           |  107 
 R/calculate_lsm.R                                 |  775 ++--
 R/check_landscape.R                               |  310 -
 R/construct_buffer.R                              |  276 -
 R/data.R                                          |  258 -
 R/extract_lsm.R                                   |  696 +--
 R/get_adjacencies.R                               |  160 
 R/get_boundaries.R                                |  212 -
 R/get_circumscribingcircle.R                      |  150 
 R/get_nearestneighbour.R                          |  150 
 R/get_patches.R                                   |  582 +--
 R/get_unique_values.R                             |  434 +-
 R/landscapemetrics-package.R                      |  103 
 R/list_lsm.R                                      |  216 -
 R/lsm_c_ai.R                                      |  294 -
 R/lsm_c_area_cv.R                                 |  258 -
 R/lsm_c_area_mn.R                                 |  260 -
 R/lsm_c_area_sd.R                                 |  258 -
 R/lsm_c_cai_cv.R                                  |  292 -
 R/lsm_c_cai_mn.R                                  |  286 -
 R/lsm_c_cai_sd.R                                  |  290 -
 R/lsm_c_circle_cv.R                               |  272 -
 R/lsm_c_circle_mn.R                               |  268 -
 R/lsm_c_circle_sd.R                               |  270 -
 R/lsm_c_clumpy.R                                  |  334 -
 R/lsm_c_cohesion.R                                |  318 -
 R/lsm_c_contig_cv.R                               |  274 -
 R/lsm_c_contig_mn.R                               |  272 -
 R/lsm_c_contig_sd.R                               |  276 -
 R/lsm_c_core_cv.R                                 |  284 -
 R/lsm_c_core_mn.R                                 |  280 -
 R/lsm_c_core_sd.R                                 |  284 -
 R/lsm_c_cpland.R                                  |  316 -
 R/lsm_c_dcad.R                                    |  312 -
 R/lsm_c_dcore_cv.R                                |  294 -
 R/lsm_c_dcore_mn.R                                |  288 -
 R/lsm_c_dcore_sd.R                                |  298 -
 R/lsm_c_division.R                                |  272 -
 R/lsm_c_ed.R                                      |  316 -
 R/lsm_c_enn_cv.R                                  |  284 -
 R/lsm_c_enn_mn.R                                  |  286 -
 R/lsm_c_enn_sd.R                                  |  286 -
 R/lsm_c_frac_cv.R                                 |  264 -
 R/lsm_c_frac_mn.R                                 |  258 -
 R/lsm_c_frac_sd.R                                 |  262 -
 R/lsm_c_gyrate_cv.R                               |  272 -
 R/lsm_c_gyrate_mn.R                               |  270 -
 R/lsm_c_gyrate_sd.R                               |  270 -
 R/lsm_c_iji.R                                     |  310 -
 R/lsm_c_lpi.R                                     |  264 -
 R/lsm_c_lsi.R                                     |  300 -
 R/lsm_c_mesh.R                                    |  278 -
 R/lsm_c_ndca.R                                    |  290 -
 R/lsm_c_nlsi.R                                    |  338 -
 R/lsm_c_np.R                                      |  260 -
 R/lsm_c_pafrac.R                                  |  350 -
 R/lsm_c_para_cv.R                                 |  258 -
 R/lsm_c_para_mn.R                                 |  258 -
 R/lsm_c_para_sd.R                                 |  258 -
 R/lsm_c_pd.R                                      |  276 -
 R/lsm_c_pladj.R                                   |  254 -
 R/lsm_c_pland.R                                   |  248 -
 R/lsm_c_shape_cv.R                                |  264 -
 R/lsm_c_shape_mn.R                                |  264 -
 R/lsm_c_shape_sd.R                                |  264 -
 R/lsm_c_split.R                                   |  270 -
 R/lsm_c_tca.R                                     |  284 -
 R/lsm_c_te.R                                      |  438 +-
 R/lsm_l_ai.R                                      |  260 -
 R/lsm_l_area_cv.R                                 |  258 -
 R/lsm_l_area_mn.R                                 |  260 -
 R/lsm_l_area_sd.R                                 |  258 -
 R/lsm_l_cai_cv.R                                  |  320 -
 R/lsm_l_cai_mn.R                                  |  314 -
 R/lsm_l_cai_sd.R                                  |  318 -
 R/lsm_l_circle_cv.R                               |  266 -
 R/lsm_l_circle_mn.R                               |  266 -
 R/lsm_l_circle_sd.R                               |  270 -
 R/lsm_l_cohesion.R                                |  294 -
 R/lsm_l_condent.R                                 |  312 -
 R/lsm_l_contag.R                                  |  316 -
 R/lsm_l_contig_cv.R                               |  270 -
 R/lsm_l_contig_mn.R                               |  268 -
 R/lsm_l_contig_sd.R                               |  270 -
 R/lsm_l_core_cv.R                                 |  314 -
 R/lsm_l_core_mn.R                                 |  310 -
 R/lsm_l_core_sd.R                                 |  312 -
 R/lsm_l_dcad.R                                    |  334 -
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 R/lsm_l_dcore_mn.R                                |  314 -
 R/lsm_l_dcore_sd.R                                |  318 -
 R/lsm_l_division.R                                |  268 -
 R/lsm_l_ed.R                                      |  308 -
 R/lsm_l_enn_cv.R                                  |  276 -
 R/lsm_l_enn_mn.R                                  |  276 -
 R/lsm_l_enn_sd.R                                  |  276 -
 R/lsm_l_ent.R                                     |  226 -
 R/lsm_l_frac_cv.R                                 |  260 -
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 R/lsm_l_gyrate_cv.R                               |  266 -
 R/lsm_l_gyrate_mn.R                               |  264 -
 R/lsm_l_gyrate_sd.R                               |  264 -
 R/lsm_l_iji.R                                     |  314 -
 R/lsm_l_joinent.R                                 |  296 -
 R/lsm_l_lpi.R                                     |  256 -
 R/lsm_l_lsi.R                                     |  296 -
 R/lsm_l_mesh.R                                    |  266 -
 R/lsm_l_msidi.R                                   |  268 -
 R/lsm_l_msiei.R                                   |  258 -
 R/lsm_l_mutinf.R                                  |  308 -
 R/lsm_l_ndca.R                                    |  312 -
 R/lsm_l_np.R                                      |  234 -
 R/lsm_l_pafrac.R                                  |  340 -
 R/lsm_l_para_cv.R                                 |  254 -
 R/lsm_l_para_mn.R                                 |  254 -
 R/lsm_l_para_sd.R                                 |  254 -
 R/lsm_l_pd.R                                      |  280 -
 R/lsm_l_pladj.R                                   |  246 -
 R/lsm_l_pr.R                                      |  212 -
 R/lsm_l_prd.R                                     |  254 -
 R/lsm_l_rpr.R                                     |  274 -
 R/lsm_l_shape_cv.R                                |  264 -
 R/lsm_l_shape_mn.R                                |  264 -
 R/lsm_l_shape_sd.R                                |  264 -
 R/lsm_l_shdi.R                                    |  240 -
 R/lsm_l_shei.R                                    |  240 -
 R/lsm_l_sidi.R                                    |  250 -
 R/lsm_l_siei.R                                    |  252 -
 R/lsm_l_split.R                                   |  268 -
 R/lsm_l_ta.R                                      |  238 -
 R/lsm_l_tca.R                                     |  308 -
 R/lsm_l_te.R                                      |  334 -
 R/lsm_p_area.R                                    |  308 -
 R/lsm_p_cai.R                                     |  362 +-
 R/lsm_p_circle.R                                  |  491 +-
 R/lsm_p_contig.R                                  |  386 +-
 R/lsm_p_core.R                                    |  427 +-
 R/lsm_p_enn.R                                     |  606 +--
 R/lsm_p_frac.R                                    |  300 -
 R/lsm_p_gyrate.R                                  |  417 +-
 R/lsm_p_ncore.R                                   |  786 ++--
 R/lsm_p_para.R                                    |  284 -
 R/lsm_p_perim.R                                   |  388 +-
 R/lsm_p_shape.R                                   |  300 -
 R/matrix_to_raster.R                              |  164 
 R/onUnload.R                                      |   16 
 R/options_landscapemetrics.R                      |   64 
 R/pad_raster.R                                    |  246 -
 R/raster_to_points.R                              |   84 
 R/sample_lsm.R                                    |  526 +-
 R/show_cores.R                                    |  570 +--
 R/show_correlation.R                              |  470 +-
 R/show_lsm.R                                      |  602 +--
 R/show_patches.R                                  |  444 +-
 build/vignette.rds                                |binary
 inst/doc/getstarted.Rmd                           |  364 +-
 inst/doc/getstarted.html                          |  106 
 man/calculate_lsm.Rd                              |   11 
 man/get_adjacencies.Rd                            |   14 
 man/list_lsm.Rd                                   |    5 
 man/sample_lsm.Rd                                 |   61 
 src/Makevars                                      |only
 src/Makevars.win                                  |only
 tests/testthat.R                                  |    8 
 tests/testthat/helper-brick.R                     |    8 
 tests/testthat/helper-landscapes.R                |   34 
 tests/testthat/helper-points.R                    |   36 
 tests/testthat/test-calculate-lsm.R               |  186 -
 tests/testthat/test-check-landscape.R             |   44 
 tests/testthat/test-construct-buffer.R            |   34 
 tests/testthat/test-extract-lsm.R                 |   66 
 tests/testthat/test-get-boundaries.R              |   76 
 tests/testthat/test-get-circumscribungcircle.R    |   60 
 tests/testthat/test-get-nearestneighbour.R        |   36 
 tests/testthat/test-get-patches.R                 |   46 
 tests/testthat/test-get-unique-values.R           |  166 
 tests/testthat/test-list-lsm.R                    |   92 
 tests/testthat/test-lsm-c-ai.R                    |   48 
 tests/testthat/test-lsm-c-area-cv.R               |   48 
 tests/testthat/test-lsm-c-area-mn.R               |   50 
 tests/testthat/test-lsm-c-area-sd.R               |   48 
 tests/testthat/test-lsm-c-ca.R                    |   52 
 tests/testthat/test-lsm-c-cai-cv.R                |   50 
 tests/testthat/test-lsm-c-cai-mn.R                |   50 
 tests/testthat/test-lsm-c-cai-sd.R                |   46 
 tests/testthat/test-lsm-c-circle-cv.R             |   52 
 tests/testthat/test-lsm-c-circle-mn.R             |   48 
 tests/testthat/test-lsm-c-circle-sd.R             |   48 
 tests/testthat/test-lsm-c-clumpy.R                |   48 
 tests/testthat/test-lsm-c-cohesion.R              |   50 
 tests/testthat/test-lsm-c-contig-cv.R             |   50 
 tests/testthat/test-lsm-c-contig-mn.R             |   50 
 tests/testthat/test-lsm-c-contig-sd.R             |   50 
 tests/testthat/test-lsm-c-core-cv.R               |   46 
 tests/testthat/test-lsm-c-core-mn.R               |   46 
 tests/testthat/test-lsm-c-core-sd.R               |   46 
 tests/testthat/test-lsm-c-cpland.R                |   48 
 tests/testthat/test-lsm-c-dcad.R                  |   48 
 tests/testthat/test-lsm-c-dcore_cv.R              |   46 
 tests/testthat/test-lsm-c-dcore_mn.R              |   46 
 tests/testthat/test-lsm-c-dcore_sd.R              |   46 
 tests/testthat/test-lsm-c-division.R              |   48 
 tests/testthat/test-lsm-c-ed.R                    |   48 
 tests/testthat/test-lsm-c-enn-cv.R                |   46 
 tests/testthat/test-lsm-c-enn-mn.R                |   46 
 tests/testthat/test-lsm-c-enn-sd.R                |   46 
 tests/testthat/test-lsm-c-frac-cv.R               |   46 
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 tests/testthat/test-lsm-c-frac-sd.R               |   46 
 tests/testthat/test-lsm-c-gyrate-cv.R             |   46 
 tests/testthat/test-lsm-c-gyrate-mn.R             |   46 
 tests/testthat/test-lsm-c-gyrate-sd.R             |   46 
 tests/testthat/test-lsm-c-iji.R                   |   58 
 tests/testthat/test-lsm-c-lpi.R                   |   48 
 tests/testthat/test-lsm-c-lsi.R                   |   48 
 tests/testthat/test-lsm-c-mesh.R                  |   48 
 tests/testthat/test-lsm-c-ndca.R                  |   48 
 tests/testthat/test-lsm-c-nlsi.R                  |   48 
 tests/testthat/test-lsm-c-np.R                    |   48 
 tests/testthat/test-lsm-c-pafrac.R                |   56 
 tests/testthat/test-lsm-c-para-cv.R               |   46 
 tests/testthat/test-lsm-c-para-mn.R               |   46 
 tests/testthat/test-lsm-c-para-sd.R               |   46 
 tests/testthat/test-lsm-c-pd.R                    |   48 
 tests/testthat/test-lsm-c-pladj.R                 |   48 
 tests/testthat/test-lsm-c-pland.R                 |   48 
 tests/testthat/test-lsm-c-shape-cv.R              |   46 
 tests/testthat/test-lsm-c-shape-mn.R              |   46 
 tests/testthat/test-lsm-c-shape-sd.R              |   46 
 tests/testthat/test-lsm-c-split.R                 |   48 
 tests/testthat/test-lsm-c-tca.R                   |   48 
 tests/testthat/test-lsm-c-te.R                    |   64 
 tests/testthat/test-lsm-l-ai.R                    |   48 
 tests/testthat/test-lsm-l-area-cv.R               |   46 
 tests/testthat/test-lsm-l-area-mn.R               |   50 
 tests/testthat/test-lsm-l-area-sd.R               |   50 
 tests/testthat/test-lsm-l-cai-cv.R                |   50 
 tests/testthat/test-lsm-l-cai-mn.R                |   50 
 tests/testthat/test-lsm-l-cai-sd.R                |   50 
 tests/testthat/test-lsm-l-circle-cv.R             |   50 
 tests/testthat/test-lsm-l-circle-mn.R             |   50 
 tests/testthat/test-lsm-l-circle-sd.R             |   50 
 tests/testthat/test-lsm-l-cohesion.R              |   48 
 tests/testthat/test-lsm-l-condent.R               |   48 
 tests/testthat/test-lsm-l-contag.R                |   56 
 tests/testthat/test-lsm-l-contig-cv.R             |   46 
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 tests/testthat/test-lsm-l-core-cv.R               |   50 
 tests/testthat/test-lsm-l-core-mn.R               |   50 
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 tests/testthat/test-lsm-l-dcad.R                  |   48 
 tests/testthat/test-lsm-l-dcore-cv.R              |   50 
 tests/testthat/test-lsm-l-dcore-mn.R              |   50 
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 tests/testthat/test-lsm-l-enn-cv.R                |   50 
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 tests/testthat/test-lsm-l-enn-sd.R                |   50 
 tests/testthat/test-lsm-l-ent.R                   |   48 
 tests/testthat/test-lsm-l-frac-cv.R               |   46 
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 tests/testthat/test-lsm-l-gyrate-sd.R             |   48 
 tests/testthat/test-lsm-l-iji.R                   |   58 
 tests/testthat/test-lsm-l-joinent.R               |   48 
 tests/testthat/test-lsm-l-lpi.R                   |   48 
 tests/testthat/test-lsm-l-lsi.R                   |   48 
 tests/testthat/test-lsm-l-mesh.R                  |   48 
 tests/testthat/test-lsm-l-msidi.R                 |   48 
 tests/testthat/test-lsm-l-msiei.R                 |   48 
 tests/testthat/test-lsm-l-mutinf.R                |   48 
 tests/testthat/test-lsm-l-ndca.R                  |   48 
 tests/testthat/test-lsm-l-np.R                    |   48 
 tests/testthat/test-lsm-l-pafrac.R                |   56 
 tests/testthat/test-lsm-l-para-cv.R               |   46 
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 tests/testthat/test-lsm-l-pd.R                    |   48 
 tests/testthat/test-lsm-l-pladj.R                 |   48 
 tests/testthat/test-lsm-l-pr.R                    |   48 
 tests/testthat/test-lsm-l-prd.R                   |   48 
 tests/testthat/test-lsm-l-rpr.R                   |   48 
 tests/testthat/test-lsm-l-shape-cv.R              |   46 
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 tests/testthat/test-lsm-l-shdi.R                  |   48 
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 tests/testthat/test-lsm-l-sidi.R                  |   48 
 tests/testthat/test-lsm-l-siei.R                  |   48 
 tests/testthat/test-lsm-l-split.R                 |   48 
 tests/testthat/test-lsm-l-ta.R                    |   48 
 tests/testthat/test-lsm-l-tca.R                   |   48 
 tests/testthat/test-lsm-l-te.R                    |   66 
 tests/testthat/test-lsm-p-area.R                  |   50 
 tests/testthat/test-lsm-p-cai.R                   |   46 
 tests/testthat/test-lsm-p-circle.R                |   54 
 tests/testthat/test-lsm-p-contig.R                |   50 
 tests/testthat/test-lsm-p-core.R                  |   61 
 tests/testthat/test-lsm-p-enn.R                   |   50 
 tests/testthat/test-lsm-p-frac.R                  |   50 
 tests/testthat/test-lsm-p-gyrate.R                |   50 
 tests/testthat/test-lsm-p-nca.R                   |   50 
 tests/testthat/test-lsm-p-para.R                  |   50 
 tests/testthat/test-lsm-p-perim.R                 |   54 
 tests/testthat/test-lsm-p-shape.R                 |   48 
 tests/testthat/test-matrix-to-raster.R            |   96 
 tests/testthat/test-pad-raster.R                  |   30 
 tests/testthat/test-raster-to-points.R            |   32 
 tests/testthat/test-rcpp-get-coocurrence-matrix.R |  204 -
 tests/testthat/test-rcpp-get-entropy.R            |   46 
 tests/testthat/test-sample-lsm.R                  |  104 
 tests/testthat/test-show-cores.R                  |   90 
 tests/testthat/test-show-correlation.R            |   42 
 tests/testthat/test-show-lsm.R                    |   78 
 tests/testthat/test-show-patches.R                |   70 
 tests/testthat/test-unique-values.R               |   26 
 tests/testthat/test_fragstats.R                   | 3884 +++++++++++-----------
 vignettes/getstarted.Rmd                          |  364 +-
 325 files changed, 29848 insertions(+), 29810 deletions(-)

More information about landscapemetrics at CRAN
Permanent link

Package mgcViz updated to version 0.1.3 with previous version 0.1.1 dated 2018-06-30

Title: Visualisations for Generalized Additive Models
Description: Extension of the 'mgcv' package, providing visual tools for Generalized Additive Models that exploit the additive structure of such models, scale to large data sets and can be used in conjunction with a wide range of response distributions. The focus is providing visual methods for better understanding the model output and for aiding model checking and development beyond simple exponential family regression. The graphical framework is based on the layering system provided by 'ggplot2'.
Author: Matteo Fasiolo [aut, cre], Raphael Nedellec [aut], Yannig Goude [ctb], Simon N. Wood [ctb]
Maintainer: Matteo Fasiolo <matteo.fasiolo@gmail.com>

Diff between mgcViz versions 0.1.1 dated 2018-06-30 and 0.1.3 dated 2019-01-23

 mgcViz-0.1.1/mgcViz/R/L_dens.R                           |only
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 mgcViz-0.1.1/mgcViz/man/l_dens.Rd                        |only
 mgcViz-0.1.3/mgcViz/DESCRIPTION                          |   21 
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 mgcViz-0.1.3/mgcViz/NAMESPACE                            |   47 
 mgcViz-0.1.3/mgcViz/R/I_compute_qq_gam.R                 |    2 
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Package Rmosek updated to version 1.3.1 with previous version 1.3.0 dated 2019-01-18

Title: The R to MOSEK Optimization Interface
Description: This is a generic meta-package designed to make the optimization facilities of MOSEK available from the R-language. The interface supports large-scale optimization of many kinds: Mixed-integer and continuous linear, second-order cone, exponential cone and power cone optimization, as well as continuous semidefinite optimization. Rmosek and the R-language are open-source projects. MOSEK is a proprietary product, but unrestricted trial and academic licenses are available.
Author: MOSEK ApS
Maintainer: Henrik A. Friberg <haf@mosek.com>

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Package MachineShop updated to version 1.1.0 with previous version 1.0.0 dated 2019-01-02

Title: Machine Learning Models and Tools
Description: Meta-package for statistical and machine learning with a common interface for model fitting, prediction, performance assessment, and presentation of results. Supports predictive modeling of numerical, categorical, and censored time-to-event outcomes and resample (bootstrap and cross-validation) estimation of model performance.
Author: Brian J Smith [aut, cre]
Maintainer: Brian J Smith <brian-j-smith@uiowa.edu>

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Package fitdistrplus updated to version 1.0-14 with previous version 1.0-11 dated 2018-09-10

Title: Help to Fit of a Parametric Distribution to Non-Censored or Censored Data
Description: Extends the fitdistr() function (of the MASS package) with several functions to help the fit of a parametric distribution to non-censored or censored data. Censored data may contain left censored, right censored and interval censored values, with several lower and upper bounds. In addition to maximum likelihood estimation (MLE), the package provides moment matching (MME), quantile matching (QME) and maximum goodness-of-fit estimation (MGE) methods (available only for non-censored data). Weighted versions of MLE, MME and QME are available. See e.g. Casella & Berger (2002). Statistical inference. Pacific Grove.
Author: Marie-Laure Delignette-Muller [aut], Christophe Dutang [aut], Regis Pouillot [ctb], Jean-Baptiste Denis [ctb], Aurelie Siberchicot [aut, cre]
Maintainer: Aurelie Siberchicot <aurelie.siberchicot@univ-lyon1.fr>

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Package imagerExtra (with last version 1.3.1) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2019-01-11 1.3.1
2018-11-28 1.3.0
2018-07-22 1.2.0
2018-07-02 1.1.1
2018-07-01 1.1.0
2018-05-29 1.0

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Package phylosignal updated to version 1.2.1 with previous version 1.2 dated 2018-01-10

Title: Exploring the Phylogenetic Signal in Continuous Traits
Description: A collection of tools to explore the phylogenetic signal in univariate and multivariate data. The package provides functions to plot traits data against a phylogenetic tree, different measures and tests for the phylogenetic signal, methods to describe where the signal is located and a phylogenetic clustering method.
Author: Francois Keck <francois.keck@gmail.com>
Maintainer: ORPHANED

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Package eurostat updated to version 3.3.1.1 with previous version 3.3.1 dated 2018-11-24

Title: Tools for Eurostat Open Data
Description: Tools to download data from the Eurostat database <http://ec.europa.eu/eurostat> together with search and manipulation utilities.
Author: Leo Lahti [aut, cre], Janne Huovari [aut], Markus Kainu [aut], Przemyslaw Biecek [aut], Joona Lehtomaki [ctb], Francois Briatte [ctb], Oliver Reiter [ctb]
Maintainer: ORPHANED

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Package surveydata updated to version 0.2.3 with previous version 0.2.2 dated 2018-12-06

Title: Tools to Work with Survey Data
Description: Data obtained from surveys contains information not only about the survey responses, but also the survey metadata, e.g. the original survey questions and the answer options. The 'surveydata' package makes it easy to keep track of this metadata, and to easily extract columns with specific questions.
Author: Andrie de Vries [aut, cre, cph]
Maintainer: Andrie de Vries <apdevries@gmail.com>

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Package colorednoise updated to version 1.0.4 with previous version 1.0.3 dated 2018-06-01

Title: Simulate Temporally Autocorrelated Populations
Description: Temporally autocorrelated populations are correlated in their vital rates (growth, death, etc.) from year to year. It is very common for populations, whether they be bacteria, plants, or humans, to be temporally autocorrelated. This poses a challenge for stochastic population modeling, because a temporally correlated population will behave differently from an uncorrelated one. This package provides tools for simulating populations with white noise (no temporal autocorrelation), red noise (positive temporal autocorrelation), and blue noise (negative temporal autocorrelation). The algebraic formulation for autocorrelated noise comes from Ruokolainen et al. (2009) <doi:10.1016/j.tree.2009.04.009>. Models for unstructured populations and for structured populations (matrix models) are available.
Author: Julia Pilowsky [aut, cre] (<https://orcid.org/0000-0002-6376-2585>)
Maintainer: Julia Pilowsky <jap2178@caa.columbia.edu>

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Package Sleuth3 updated to version 1.0-2.1 with previous version 1.0-2 dated 2016-06-15

Title: Data Sets from Ramsey and Schafer's "Statistical Sleuth (3rd Ed)"
Description: Data sets from Ramsey, F.L. and Schafer, D.W. (2013), "The Statistical Sleuth: A Course in Methods of Data Analysis (3rd ed)", Cengage Learning.
Author: Original by F.L. Ramsey and D.W. Schafer; modifications by Daniel W. Schafer, Jeannie Sifneos and Berwin A. Turlach; vignettes contributed by Nicholas Horton, Linda Loi, Kate Aloisio and Ruobing Zhang, with corrections by Randall Pruim
Maintainer: ORPHANED

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Package Sleuth2 updated to version 2.0-4.1 with previous version 2.0-4 dated 2016-06-15

Title: Data Sets from Ramsey and Schafer's "Statistical Sleuth (2nd Ed)"
Description: Data sets from Ramsey, F.L. and Schafer, D.W. (2002), "The Statistical Sleuth: A Course in Methods of Data Analysis (2nd ed)", Duxbury.
Author: Original by F.L. Ramsey and D.W. Schafer; modifications by Daniel W. Schafer, Jeannie Sifneos and Berwin A. Turlach; vignettes contributed by Nicholas Horton, Kate Aloisio and Ruobing Zhang, with corrections by Randall Pruim
Maintainer: ORPHANED

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Package missMDA updated to version 1.14 with previous version 1.13 dated 2018-06-25

Title: Handling Missing Values with Multivariate Data Analysis
Description: Imputation of incomplete continuous or categorical datasets; Missing values are imputed with a principal component analysis (PCA), a multiple correspondence analysis (MCA) model or a multiple factor analysis (MFA) model; Perform multiple imputation with and in PCA or MCA.
Author: Francois Husson, Julie Josse
Maintainer: Francois Husson <husson@agrocampus-ouest.fr>

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Package imager updated to version 0.41.2 with previous version 0.41.1 dated 2018-05-30

Title: Image Processing Library Based on 'CImg'
Description: Fast image processing for images in up to 4 dimensions (two spatial dimensions, one time/depth dimension, one colour dimension). Provides most traditional image processing tools (filtering, morphology, transformations, etc.) as well as various functions for easily analysing image data using R. The package wraps 'CImg', <http://cimg.eu>, a simple, modern C++ library for image processing.
Author: Simon Barthelme [aut, cre], David Tschumperle [ctb], Jan Wijffels [ctb], Haz Edine Assemlal [ctb]
Maintainer: ORPHANED

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Package getTBinR updated to version 0.5.7 with previous version 0.5.6 dated 2018-11-15

Title: Get WHO Tuberculosis Data
Description: Facilitates easy import of analysis ready World Health Organisation Tuberculosis data and provides plotting functions for exploratory data analysis.
Author: Sam Abbott [aut, cre] (<https://orcid.org/0000-0001-8057-8037>)
Maintainer: Sam Abbott <contact@samabbott.co.uk>

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Package spmoran updated to version 0.1.6.1 with previous version 0.1.6 dated 2018-10-06

Title: Moran's Eigenvector-Based Spatial Regression Models
Description: Functions for estimating Moran's eigenvector-based spatial regression models. For details see Murakami (2018) <arXiv:1703.04467>.
Author: Daisuke Murakami
Maintainer: Daisuke Murakami <dmuraka@ism.ac.jp>

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Package logging updated to version 0.9-106 with previous version 0.9-105 dated 2019-01-16

Title: R Logging Package
Description: Pure R implementation of the ubiquitous log4j package. It offers hierarchic loggers, multiple handlers per logger, level based filtering, space handling in messages and custom formatting.
Author: Mario Frasca [aut], Walerian Sokolowski [cre]
Maintainer: Walerian Sokolowski <r-logging@wlogsolutions.com>

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Package BHSBVAR updated to version 1.0.1 with previous version 1.0.0 dated 2018-11-19

Title: Structural Bayesian Vector Autoregression Models
Description: Provides a function for running Structural Bayesian Vector Autoregression models with the method developed by Baumeister and Hamilton (2015) <doi:10.3982/ECTA12356>, Baumeister and Hamilton (2017) <doi:10.3386/w24167>, and Baumeister and Hamilton (2018) <doi:10.1016/j.jmoneco.2018.06.005>. Functions for plotting impulse responses, historical decompositions, and posterior distributions of model parameters are also provided.
Author: Paul Richardson
Maintainer: Paul Richardson <p.richardson.54391@gmail.com>

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New package woeR with initial version 0.2.1
Package: woeR
Title: Weight of Evidence Based Segmentation of a Variable
Version: 0.2.1
Author: Kashish Soien
Maintainer: Kashish Soien <kashish.soien19@gmail.com>
Description: Segment a numeric variable based on a dichotomous dependent variable by using the weight of evidence (WOE) approach (Ref: Siddiqi, N. (2006) <doi:10.1002/9781119201731.biblio>). The underlying algorithm adopts a recursive approach to create segments that are diverse in respect of their WOE values and meet the demands of user-defined parameters. The algorithm also aims to maintain a monotonic trend in WOE values of consecutive segments. As such, it can be particularly helpful in improving robustness of linear and logistic regression models.
Imports: dplyr
Suggests: smbinning
Depends: R (>= 3.4.0)
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
BugReports: https://github.com/kraken19/woeR/issues
NeedsCompilation: no
Packaged: 2019-01-23 00:15:53 UTC; kashish
Repository: CRAN
Date/Publication: 2019-01-23 08:30:04 UTC

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Package tram updated to version 0.2-4 with previous version 0.2-3 dated 2018-09-18

Title: Transformation Models
Description: Formula-based user-interfaces to specific transformation models implemented in package 'mlt'. Available models include Cox models, some parametric survival models (Weibull, etc.), models for ordered categorical variables, normal and non-normal (Box-Cox type) linear models, and continuous outcome logistic regression (Lohse et al., 2017, <DOI:10.12688/f1000research.12934.1>). The underlying theory is described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291>.
Author: Torsten Hothorn [aut, cre] (<https://orcid.org/0000-0001-8301-0471>)
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>

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Package homomorpheR updated to version 0.2-2 with previous version 0.2-1 dated 2017-04-28

Title: Homomorphic Computations in R
Description: Homomorphic computations in R for privacy-preserving applications. Currently only the Paillier Scheme is implemented.
Author: Balasubramanian Narasimhan [aut, cre]
Maintainer: Balasubramanian Narasimhan <naras@stat.Stanford.EDU>

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Package ggmap updated to version 2.6.2 with previous version 2.6.1 dated 2016-01-22

Title: Spatial Visualization with ggplot2
Description: A collection of functions to visualize spatial data and models on top of static maps from various online sources (e.g Google Maps and Stamen Maps). It includes tools common to those tasks, including functions for geolocation and routing.
Author: David Kahle [aut, cre], Hadley Wickham [aut]
Maintainer: ORPHANED

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Package BART updated to version 2.2 with previous version 2.1 dated 2018-11-30

Title: Bayesian Additive Regression Trees
Description: Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes. For more information on BART, see Chipman, George and McCulloch (2010) <doi:10.1214/09-AOAS285> and Sparapani, Logan, McCulloch and Laud (2016) <doi:10.1002/sim.6893>.
Author: Robert McCulloch [aut], Rodney Sparapani [aut, cre], Robert Gramacy [aut], Charles Spanbauer [aut], Matthew Pratola [aut]
Maintainer: Rodney Sparapani <rsparapa@mcw.edu>

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Package natserv updated to version 0.3.0 with previous version 0.1.4 dated 2017-01-03

Title: 'NatureServe' Interface
Description: Interface to 'NatureServe' (<http://www.natureserve.org>). Includes methods to get data, image metadata, search taxonomic names, and make maps.
Author: Scott Chamberlain [aut, cre] (<https://orcid.org/0000-0003-1444-9135>)
Maintainer: Scott Chamberlain <myrmecocystus@gmail.com>

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Package g3viz updated to version 0.1.4 with previous version 0.1.3 dated 2018-08-30

Title: Visualize Genomic Mutation Data Using an Interactive Lollipop Diagram
Description: R interface for 'g3viz' JavaScript library. Using an interactive lollipop diagram to visualize genomic mutation data in a web browser.
Author: Xin Guo [aut, cre]
Maintainer: Xin Guo <phoeguo@gmail.com>

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Package face updated to version 0.1-5 with previous version 0.1-4 dated 2018-01-19

Title: Fast Covariance Estimation for Sparse Functional Data
Description: We implement the Fast Covariance Estimation for Sparse Functional Data paper published in Statistics and Computing <doi: 10.1007/s11222-017-9744-8>.
Author: Luo Xiao [aut,cre], Cai Li [aut], William Checkley [aut], Ciprian Crainiceanu [aut]
Maintainer: Luo Xiao <lxiao5@ncsu.edu>

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Package easyPubMed updated to version 2.11 with previous version 2.5 dated 2018-05-06

Title: Search and Retrieve Scientific Publication Records from PubMed
Description: Query NCBI Entrez and retrieve PubMed records in XML or text format. Process PubMed records by extracting and aggregating data from selected fields. A large number of records can be easily downloaded via this simple-to-use interface to the NCBI PubMed API.
Author: Damiano Fantini
Maintainer: Damiano Fantini <damiano.fantini@gmail.com>

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Package carfima updated to version 2.0.1 with previous version 2.0.0 dated 2018-11-01

Title: Continuous-Time Fractionally Integrated ARMA Process for Irregularly Spaced Long-Memory Time Series Data
Description: We provide a toolbox to fit a continuous-time fractionally integrated ARMA process (CARFIMA) on univariate and irregularly spaced time series data via frequentist or Bayesian machinery. A general-order CARFIMA(p, H, q) model for p>q is specified in Tsai and Chan (2005) <doi:10.1111/j.1467-9868.2005.00522.x> and it involves (p+q+2) unknown model parameters, i.e., p AR parameters, q MA parameters, Hurst parameter H, and process uncertainty (standard deviation) sigma. The package produces their maximum likelihood estimates and asymptotic uncertainties using a global optimizer called the differential evolution algorithm. It also produces their posterior distributions via Metropolis within a Gibbs sampler equipped with adaptive Markov chain Monte Carlo for posterior sampling. These fitting procedures, however, may produce numerical errors if p>2. The toolbox also contains a function to simulate discrete time series data from CARFIMA(p, H, q) process given the model parameters and observation times.
Author: Hyungsuk Tak [aut] (<https://orcid.org/0000-0003-0334-8742>), Henghsiu Tsai [aut], Kisung You [aut, cre] (<https://orcid.org/0000-0002-8584-459X>)
Maintainer: Kisung You <kyou@nd.edu>

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Package placement (with last version 0.1.1) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2016-07-27 0.1.1

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Package census (with last version 0.2.0) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2017-10-26 0.2.0
2017-10-23 0.1.0

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Package glmmBUGS (with last version 2.4.2) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2018-05-01 2.4.2
2016-09-21 2.4.0
2014-01-02 2.3
2012-01-13 2.0
2010-11-08 1.9
2010-10-19 1.8
2010-10-04 1.7
2009-10-18 1.6.4
2009-04-26 1.6
2009-01-13 1.5
2009-01-12 1.4
2008-11-25 1.2
2008-11-21 1.1
2008-09-09 1.0

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Package repijson (with last version 0.1.0) was removed from CRAN

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2015-06-15 0.1.0

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